Covariances of density probability distribution functions. Lessons from hierarchical models

TL;DR

This paper models the covariance matrix of the one-point density PDF in cosmological density fields using hierarchical models, highlighting the importance of large-scale and short-distance effects for accurate likelihood estimation.

Contribution

It introduces a detailed model for the covariance matrix of the density PDF in hierarchical models, including the impact of short-distance effects and validation with a toy model.

Findings

Covariance matrix elements relate to the spatial average of the two-point density PDF.

Large-scale effects dominate the supersample effects.

Adding short-distance contributions improves likelihood modeling.

Abstract

Context: Statistical properties of the cosmic density fields are to a large extent encoded in the shape of the one-point density probability distribution functions (PDF). In order to successfully exploit such observables, a detailed functional form of the covariance matrix of the one-point PDF is needed. Aims: The objectives are to model the properties of this covariance for general stochastic density fields in a cosmological context. Methods: Leading and subleading contributions to the covariance were identified within a large class of models, the so-called hierarchical models. The validity of the proposed forms for the covariance matrix was assessed with the help of a toy model, the minimum tree model, for which a corpus of exact results could be obtained (forms of the one- and two-point PDF, large-scale density-bias functions, and full covariance matrix of the one-point PDF). Results: It is first shown that the covariance matrix elements are directly related to the spatial average of the two-point density PDF within the sample. The dominant contribution to this average is explicitly given for hierarchical models, which leads to the construction of specific density-bias functions. However, this contribution alone cannot be used to construct an operational likelihood function. Short distance effects are found to be have an important impact but are more difficult to derive as they depend more on the details of the model. However, a simple and generic form of these contributions is proposed. Detailed comparisons in the context of the Rayleigh-Levy flight model show that the large-scale effects capture the bulk of the supersample effects and that, by adding the short-distance contributions, a qualitatively correct model of the likelihood function can be obtained.